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Predicting Outcomes of Patients in Japan After First Acute Stroke Using a Simple Model

Inouye, Masayuki MD

American Journal of Physical Medicine and Rehabilitation: September 2001 - Volume 80 - Issue 9 - p 645-649
Research Articles
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Inouye M: Predicting outcomes of patients in Japan after first acute stroke using a simple model. Am J Phys Med Rehabil 2001;80:645–649.

Objective: Prediction of patient outcome can be useful as an aid to clinical decision making. Many studies, including my own, have constructed predictive multivariate models for outcome following stroke rehabilitation therapy, but these have often required several minutes work with a pocket calculator. The aim is to develop a simple, easy-to-use model that has strong predictive power.

Methods: Four hundred sixty-four consecutive patients with first stroke who were admitted to a rehabilitation hospital during a period of 19 mo were enrolled in the study. Sex, age, the stroke type, Functional Independence Measure total score on admission (X), onset to admission interval (number of days from stroke onset to rehabilitation admission), and length of rehabilitation hospital stay (number of days from hospital admission to discharge) were the independent variables. Functional Independence Measure total score at discharge (Y) was the dependent variable.

Results: Stepwise multiple regression analysis resulted in the model containing age (P < 0.0001), X (P < 0.0001), and onset to admission interval (P < 0.0001). The equation was:Y = 68.6 − 0.32 (age) + 0.80 X − 0.13 (onset to admission interval), a multiple correlation coefficient (R) = 0.82, and a multiple correlation coefficient squared (R2) = 0.68. Simple regression analysis revealed the relation between X and Y:Y = 0.85 X + 37.36, and R = 0.80, R2 = 0.64. In fact, plots of Xvs.Y were nonlinear, but seemed to be able to be linearized by some form of equation. It was found that there is a linear relation between log X and Y. The equation is Y = 106.88 x − 95.35, where x = log X, R = 0.84, and R2 = 0.70. The correlation is improved by a regression analysis of a natural logarithmic transformation of X (R = 0.84 vs.R = 0.82).

Conclusion: The results in this study confirm that the simple regression model using a logarithmic transformation of X (R = 0.84) has predictive power over the simple regression model (R = 0.80). This model is well validated and clinically useful.

From the Department of Internal Medicine, Hyogo Rehabilitation Center Hospital, Kobe, Japan.

FIMTMis a trademark of the Uniform Data System for Medical Rehabilitation, a division of UB Foundation Activities, Inc.

All correspondence and requests for reprints should be addressed to Masayuki Inouye, MD, Department of Internal Medicine, Hyogo Rehabilitation Center Hospital, Akebono-cho 1070, Nishi-ku, Kobe 651-2181, Japan.

© 2001 Lippincott Williams & Wilkins, Inc.